Mounting scientific evidence suggests that human unsustainable consumption may pose a significant threat to humans and the wider environment. The rapid growth of human populations and accelerated industrialization processes brings tremendous consumption of natural resources and broader negative effects on the environment (Liu et al., 2014). According to Jorgenson (2014), human-induced energy use and consumption is the main contributor to global climate change. In order to prevent more detrimental impacts in the future, human beings are faced with the imperative to move their consumption toward sustainability.

The study of sustainable consumption has attracted the broad attention of scholars, with the application of different types of data. Questionnaire survey data have been widely used in solving sustainable consumption problems, such as sustainability in electricity consumption (Wang et al., 2011), waste disposal (Pek and Jamal, 2011), travel (Salonen et al., 2014), and green product purchasing (Kumar et al., 2017). Public statistical data are another source for exploring the pathways of sustainable consumption, particularly from the perspective of national (Kanter et al., 2016) and provincial levels (Fang et al., 2015). However, sustainable consumption is a complicated issue, and would be better considered in a systematic way. Single source or small scale of data is limited in explaining issues of sustainable consumption comprehensively. Future studies are needed to acquire more data to understand sustainable consumption from different perspectives.

Luckily, big data and the corresponding processing technology have infiltrated into daily human life, and can provide more information for humans to choose more sustainable modes of consumption. With the application of big data, humans can easily access alternatives like sustainable and low carbon consumption modes and evaluate if their daily behavior and activities are sufficiently sustainable. Thus, it is emphasized that the way to achieve sustainability and low carbon consumption requires consideration of big data embedded in a large societal system.

However, there has been a lack of application of big data in explaining human behavior or activities of sustainable consumption. Most extant studies discuss sustainable behavior or activities of human consumption using data from single sources (Saphores et al., 2012). Embedding multiple types of data or data from different sources in a single study is an emerging trend, and can strength the reliability of a study. For the data on the Internet, in particular, it can supplement the traditional studies of sustainable consumption using survey data or statistical data with new insights and perspectives. These data are combined to help understand people’s willingness and behavior in performing sustainable consumption.

The Journal of Cleaner Production (JCLP) has been publishing articles related to sustainable consumption studies in a big data context. Journal topics include big-data-related study of cleaner manufacturing and maintenance processes of complex products (Zhang et al., 2017), optimization models for green supply chains (Zhao et al., 2017), service parts management (Boone et al., in press), predictive sustainable control in corporate management (Seele, in press), construction waste management (Lu et al., 2016), and marginal electricity in real-time minimization of distributed data emissions (Dandres et al., 2017). More and more researchers and practitioners of sustainable studies have chosen to publish their research results and findings with the application of big data in JCLP. In 2016, in particular, JCLP published a special volume entitled “Improving natural resource management and human health to ensure sustainable societal development based upon insights gained from working within Big Data Environments.” However, this special volume focused primarily on the application of big data in the field of natural resource allocation and utilization, while barely discussing how big data would support sustainable consumption. Since sustainable consumption is an important direction for JCLP, a special issue on sustainable consumption and big data in JCLP is also necessary, and will bring JCLP to the attention of the researchers in this area and encourage more of them to publish their research in the journal.

On this basis, this special volume presents the role of big data in solving the problems of sustainable consumption; in addition, it welcomes submissions of high-quality papers that present theoretical frameworks, methodology, and case studies of the application of big data or multiple types of data in studies of sustainable consumption from a single- or cross-country perspective.

This “Call for Papers” (CfPs) for a special volume (SV) of the JCLP provides opportunities for scholars, practitioners, government officials, and industrialists to discuss sustainable consumption in conjunction with the concept of “Big Data.” Big data in this SV includes, but is not limited to, large-scale data, massive data, data from multiple sources, real-time data, and cloud web/computing data. Under the context of, or with the application of, big data, prospective authors are challenged to investigate and evaluate the current situation of sustainable consumption with quality or quantity study. This SV will attract authors who wish to build on the application of big data concepts and frameworks, policies, methods, and results that promote sustainable consumption. Papers may be based on comprehensive literature reviews or theoretical and empirical investigations with national and/or international focus.

Energy consumption is closely related to daily human life, including electricity consumption for lighting, natural gas consumption for cooking, fuel/electricity consumption for heating, etc. Appropriate subjects for this theme include the use of big data methodology to explore sustainable energy consumption in daily human life. For example, the real-time data of electricity consumption from smart power grids provide more reliable information for daily human electricity use. These big data can help scholars understand residential energy consumption more accurately. Therefore, papers that discuss big data technology used in studies of sustainable energy consumption, of multiple data sources for energy consumption, of the influence of big data on residential energy consumption, and other related topics are welcome. Topics of interest in this theme include, but are not limited to, the following:

Travel and transportation comprise one of the important contributors to energy consumption. Low carbon travel has been imperative to achieve sustainability in daily human life. Big data methodology provides efficient tools to make daily travel cleaner and low carbon. For example, real-time information on traffic can help people choose the best path to a destination, which saves both time and energy. Scholars can also use big data on travel to make their research more reliable. Desired submissions for this theme would include research on low carbon travel in the context of big data or on using big data methodology. Topics of interest in this theme include, but are not limited to, the following:

Waste recycling is a sustainable way to save natural resources and raise the efficiency of materials. It reflects sustainable disposal of residues after residential consumption. In recent years, waste recycling through the Internet platform has emerged in many countries, including China. China, in fact, has built a database for every professionally recycled type of electrical and electronic waste. Thus, massive data and information can be collected and analyzed to explore residential waste recycling. Appropriate submissions for this theme would include research related to waste recycling in the context of big data or using the methodology of big data. Topics of interest in this theme include, but are not limited to, the following:

Financial cost will exist during the transition from traditional consumption modes to sustainable consumption, particularly when a new technology is employed. People are not likely to choose sustainable consumption if it is too expensive. Simultaneously, sustainable consumption could bring about positive environmental outcomes, such as improvement of damaged environments and less resource depletion. By comprehensively evaluating cost and performance, people can make it easier to participate in sustainable consumption. Currently, climate change has become a global issue, and the cost and potential performance in response to climate change is important information for policymakers to consider in the design of carbon emission pathways and sustainable consumption modes. Big data can provide more information support to efforts to make cost and performance evaluations easier and more accurate. This theme has been designed to encourage authors to submit manuscripts that illuminate how to evaluate the cost and performance of sustainable consumption in a big data environment or with the application of big data technology. Topics of interest in this theme include, but are not limited to, the following:

Topics of interest for this theme are focused on how big data methodology can be used in the study of sustainable consumption. This theme will discuss sustainable consumption focused on, but not be limited to, the following big data methodologies: Web/Internet data mining technology, embedding technology of different types of data, and searching technology for data from multiple sources. The prospects for how big data methodology would be employed to solve research problems in sustainable consumption in future studies are also welcome. Topics of interest in this theme include, but are not limited, to the following:

The success of sustainable consumption cannot be prolonged without proper management and policies. It is important to measure and evaluate the positive and negative effects of current policies, and design better policies for the future. Big data provide new approaches and/or data support to conduct policy modeling and simulation. The design of, as well as methods of measuring the effect of, sustainable consumption policies in the context of big data is important for future studies. Papers on this topic should focus on policies and management of sustainable consumption in the context of big data. Papers that employ big data methodology in the policy modeling of sustainable consumption are also welcome. Topics of interest in this theme include, but are not limited to, the following: